Journal
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
Volume 404, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.cam.2020.113181
Keywords
Minimum penalized phi-divergence; Model selection; Multinomial data
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Funding
- Spanish Ministry of Economy and Competitiveness [CTM2015-68276-R]
- Spanish Ministry of Economy, Industry and Competitiveness, ERDF [MTM2017-89422-P]
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This study proposes a test approach based on penalized phi-divergences to address the model selection problem for multinomial data. By deriving the null distribution and providing decision rules, it is possible to determine which model better explains the available data. The practical behavior of the approach is evaluated through simulation experiments and applied to a real data set related to social preference classification.
A test approach to the model selection problem for multinomial data based on penalized phi-divergences is proposed. The test statistic is a sample version of the difference of the distances between the population and each competing model. The null distribution of the test statistic is derived, showing that it depends on whether the competing models intersect or not and whether certain parameter is positive or not. All possible cases are characterized, and we give rules to decide if a model provides a better explanation for the available data than the other. The practical behavior of the proposal is evaluated by means of an extensive simulation experiment. The method is applied to a real data set related to the classification of individuals according to their social preferences. (c) 2020 Elsevier B.V. All rights reserved.
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